Epilepsy Research in Mali: A Pilot Pharmacokinetics Study on First-Line Antiepileptic Drug Treatment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The indication and benefit of plasma level of antiepileptic (AEDs) has been debating in the monitoring of people living with epilepsy and the epilepsy treatment gap has largely been documented in developed countries. This study was aimed to highlight the epilepsy treatment gap between rural and urban Mali. METHODS: We conducted a pilot study on AEDs treatment from September 2016 to May 2019. For 6 months, 120 children and young adults living with epilepsy (rural site, 90; urban site, 30) received phenobarbital, valproic acid and/or carbamazepine. At our rural study site, we determined the AED plasma levels, monitored the frequency, severity and the duration of seizure, and administered monthly the McGill quality of life questionnaire. At our urban study site, each patient underwent an electroencephalogram and brain computed tomography scan without close monitoring. RESULTS: <0.0001. By M3, seizures (n=90) were <1/month in 26.7%, and lasted less than 1 minute in 16.7%. After a yearlong follow up, all 90 patients reported a good or excellent quality of life. At our urban study site, patients (n=30) were on carbamazepine and valproid acid in 66.67% and monotherapy (carbamazepine) in 33.33%. By November 2018, only six out 30 patients (on bi-therapy) were still taking their medications. CONCLUSIONS: Epilepsy diagnostic and treatment are a real concern in Mali. Our data showed appropriate AED treatment with close follow up resulted in a better quality of life of patients in rural Mali. We will promote the approach of personalized medicine in AED treatment in Mali.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".